4 research outputs found
Robustness analysis of fractional order PID for an electrical aerial platform
This work was performed to objectively measure and assess the robustness and tracking performance of fractional order of proportional,
integral and derivative (FOPID) controller as compared to the conventional PID control. In satellite research and development, the
satellite undergoes numerous tests such as thermal, acoustic and vibration tests in the cleanroom environment. However, due to space
limitation in the cleanroom and the sensitive components of the satellite, it requires vibration-free, smooth and precise motion when handling
the satellite. In addition, measurement interference might occur due to cable routing during procedures or tasks performed by an
operator. Unlike the previous work, the robustness analysis of FOPID controller was not systematically conducted. In this paper, the
analysis took into account the actuator dynamics, and various tests were considered to measure the robustness of FOPID controller. The
designed FOPID controller was implemented on the scissor-type lifting mechanism of motorized adjustable vertical platform (MAVeP)
model, and its performance was compared with the traditional PID controller. A comprehensive verification using MATLAB and Solidworks
was carried out to generate the model and conduct the analysis. Both controllers were initially tuned using Nichol-Ziegler technique,
and the additional FOPID controller parameters was tuned using the Astrom-Hagglund method. From the simulation work, it was
found that the FOPID controller’s tracking error was reduced between 10 % - 50 % for the disturbance rejection tests and reference to
disturbance ratio (RDR) spectrum was higher as compared to PID. The analysis in this paper was predicted to be the main driver to
implement FOPID controller in the complex system in the industry, especially for sensitive material handling and transportation such as
satellite
Global plant characterisation and distribution with evolution and climate
Since Arrhenius published seminal work in 1921, research interest in the description of plant traits and grouped characteristics of plant species has grown, underpinning diversity in trophic levels. Geographic exploration and diversity studies prior to and after 1921 culminated in biological, chemical and computer-simulated approaches describing rudiments of growth patterns within dynamic conditions of Earth. This thesis has two parts:- classical theory and multidisciplinary fusion to give mathematical strength to characterising plant species in space and time.Individual plant species occurrences are used to obtain a Species-Area Relationship. The use of both Boolean and logic-based mathematics is then integrated to describe classical methods and propose fuzzy logic control to predict species ordination. Having demonstrated a lack of significance between species and area for data modelled in this thesis a logic based approach is taken. Mamdani and T-S-K fuzzy system stability is verified by application to individual plant occurrences, validated by a multiple interfaced data portal. Quantitative mathematical models are differentiated with a genetic programming approach, enabling visualisation of multi-objective dispersal of plant strategies, plant metabolism and life-forms within the water-energy dynamic of a fixed time-scale scenario. The distributions of plant characteristics are functionally enriched through the use of Gaussian process models. A generic framework of a Geographic Information System is used to visualise distributions and it is noted that such systems can be used to assist in design and implementation of policies. The study has made use of field based data and the application of mathematic methods is shown to be appropriate and generative in the description of characteristics of plant species, with the aim of application of plant strategies, life-forms and photosynthetic types to a global framework. Novel application of fuzzy logic and related mathematic method to plant distribution and characteristics has been shown on a global scale. Quantification of the uncertainty gives novel insight through consequent trophic levels of biological systems, with great relevance to mathematic and geographic subject development. Informative value of Z matrices of plant distribution is increased substantiating sustainability and conservation policy value to ecosystems and human populations dependent upon them for their needs.Key words: sustainability, conservation policy, Boolean and logic-based, fuzzy logic, genetic programming, multi-objective dispersal, strategies, metabolism, life-forms
Inferential active disturbance rejection control of distillation columns
PhD ThesisThe distillation column is an important processing unit in the chemical and oil refining
industry. Distillation is the most widely employed separation method in the world’s oil plants,
chemical and petrochemical industrial facilities. The main drawback of the technique is high
energy consumption, which leads to high production costs. Therefore, distillation columns are
required to be controlled close to the desired steady state conditions because of economic
incentives. Most industrial distillation columns are currently controlled by conventional multi-loop
controllers such as proportional-integral-derivative (PID) controllers, which have several
shortcomings such as difficulty coping with sudden set-point jumps, complications due to the
integral term (I), and performance degradation due to the effect of noise on the derivative term
(D). The control of ill-conditioned and strongly non-linear plants such as high purity distillation
needs advanced control schemes for high control performance. This thesis investigates the use of
active disturbance rejection control (ADRC) for product composition control in distillation
columns. To the author’s knowledge, there are few reported applications of ADRC in the chemical
industry. Most ADRC applications are in electrical, robotics and others. Therefore, this research
will be the first to apply the ADRC scheme in a common chemical processing unit, and can be
considered as a first contribution of this research.
Initially, both PI and ADRC schemes are developed and implemented on the Wood–Berry
distillation column transfer function model, on a simulated binary distillation column based on a
detailed mechanistic model, and on a simulated heat integrated distillation column (HIDiC) based
on a detailed mechanistic model. Process reaction curve method and system identification tools
are used to obtain the 2Ă—2 multi-input multi-output (MIMO) transfer function of both binary and
HIDiC for the purpose of PI tuning where the biggest log-modulus tuning (BLT) method is used.
Then, the control performance of ADRC is compared to that of the traditional PI control in terms
of set-point tracking and disturbance rejection. The simulation result clearly indicates that the
ADRC gives better control performance than PI control in all three case studies.
The long time delay associated with product composition analysers in distillation columns
such as gas chromatography deteriorates the overall control performance of the ADRC scheme.
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To overcome this issue an inferential ADRC scheme is proposed and can be considered as a second
contribution of this research. The tray temperatures of distillation columns are used to estimate
both the top and bottom product compositions that are difficult to measure on-line without a time
delay. Due to the strong correlation that exists in the tray temperature data, principal component
regression (PCR) and partial least square (PLS) are used to build the soft sensors, which are then
integrated into the ADRC. In order to overcome control offsets caused by the discrepancy between
soft sensor estimation and actual compositions measurement, an intermittent mean updating
technique is used to correct both the PCR and PLS model predictions. Furthermore, no significant
differences were observed from the simulation results in the prediction errors reported by both
PCR and PLS.
The proposed inferential ADRC scheme shows effective and promising results in dealing
with non-linear systems with a large measurement delay, where the ADRC has the ability to
accommodate both internal uncertainties and external disturbances by treating the impact from
both factors as total disturbances that will then be estimated using the extended state observer
(ESO) and cancelled out by the control law. The inferential ADRC control scheme provides tighter
product composition control that will lead to reduced energy consumption and hence increase the
distillation profitability. A binary distillation column for separating a methanol–water mixture and
an HIDiC for separating a benzene–toluene mixture are used to verify the developed inferential
ADRC control scheme.Petroleum Development of Oman (PDO) for their generous support and
scholarshi